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The analysis of the psoas muscle in morphological and functional imaging hasproved to be an accurate approach to assess sarcopenia, i.e. a systemic loss ofskeletal muscle mass and function that may be correlated to multifactorialetiological aspects. The inclusion of sarcopenia assessment into a radiologicalworkflow would need the implementation of computational pipelines for imageprocessing that guarantee segmentation reliability and a significant degree ofautomation. The present study utilizes three-dimensional numerical schemes forpsoas segmentation in low-dose X-ray computed tomography images. Specifically,here we focused on the level set methodology and compared the performances oftwo standard approaches, a classical evolution model and a three-dimensiongeodesic model, with the performances of an original first-order modificationof this latter one. The results of this analysis show that these gradient-basedschemes guarantee reliability with respect to manual segmentation and that thefirst-order scheme requires a computational burden that is significantlysmaller than the one needed by the second-order approach.

Three-dimensional numerical schemes for the segmentation of the psoas muscle in X-ray computed tomography images